Wentao Wu developed a deterministic sampling enhancement for the apple/axlearn repository, focusing on improving reproducibility in model evaluation workflows. He introduced a new parameter to the top_k_logits function in Python, enabling deterministic tie-breaking when k equals one. This approach allows the function to return either all tied logits or the smallest index, addressing ambiguity in sampling outcomes and supporting more consistent experiment results. Wentao updated the function signature and tie-breaking logic, and expanded test coverage to validate deterministic behavior and edge cases. His work demonstrated depth in data processing and machine learning, emphasizing robust, reproducible engineering practices.